Overview

Dataset statistics

Number of variables15
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory136.8 B

Variable types

Categorical1
Text1
Numeric13

Dataset

Description전국 경찰관서에 고소, 고발, 인지 등으로 형사입건된 사건의 발생, 검거, 피의자에 대한 죄종별 분석 현황
Author경찰청
URLhttps://www.data.go.kr/data/3074452/fileData.do

Alerts

현행범 is highly overall correlated with 신고-피해자신고 and 10 other fieldsHigh correlation
신고-피해자신고 is highly overall correlated with 현행범 and 10 other fieldsHigh correlation
신고-고소 is highly overall correlated with 현행범 and 5 other fieldsHigh correlation
신고-고발 is highly overall correlated with 신고-진정,투서 and 3 other fieldsHigh correlation
신고-자수 is highly overall correlated with 현행범 and 10 other fieldsHigh correlation
신고-진정,투서 is highly overall correlated with 현행범 and 10 other fieldsHigh correlation
신고-타인신고 is highly overall correlated with 현행범 and 9 other fieldsHigh correlation
미신고-불심검문 is highly overall correlated with 현행범 and 10 other fieldsHigh correlation
미신고-피해품발견 is highly overall correlated with 현행범 and 10 other fieldsHigh correlation
미신고-변사체 is highly overall correlated with 신고-자수 and 1 other fieldsHigh correlation
미신고-탐문정보 is highly overall correlated with 현행범 and 10 other fieldsHigh correlation
미신고-여죄 is highly overall correlated with 현행범 and 10 other fieldsHigh correlation
미신고-기타 is highly overall correlated with 현행범 and 10 other fieldsHigh correlation
대분류 is highly overall correlated with 현행범 and 10 other fieldsHigh correlation
중분류 has unique valuesUnique
신고-고소 has unique valuesUnique
현행범 has 4 (11.4%) zerosZeros
신고-피해자신고 has 1 (2.9%) zerosZeros
신고-고발 has 3 (8.6%) zerosZeros
신고-자수 has 8 (22.9%) zerosZeros
신고-진정,투서 has 1 (2.9%) zerosZeros
미신고-불심검문 has 9 (25.7%) zerosZeros
미신고-피해품발견 has 20 (57.1%) zerosZeros
미신고-변사체 has 22 (62.9%) zerosZeros
미신고-탐문정보 has 1 (2.9%) zerosZeros
미신고-여죄 has 4 (11.4%) zerosZeros

Reproduction

Analysis started2023-12-12 09:31:15.148343
Analysis finished2023-12-12 09:31:35.956256
Duration20.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
지능범죄
강력범죄
폭력범죄
풍속범죄
절도범죄
Other values (9)

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique10 ?
Unique (%)28.6%

Sample

1st row강력범죄
2nd row강력범죄
3rd row강력범죄
4th row강력범죄
5th row강력범죄

Common Values

ValueCountFrequency (%)
지능범죄 9
25.7%
강력범죄 7
20.0%
폭력범죄 7
20.0%
풍속범죄 2
 
5.7%
절도범죄 1
 
2.9%
마약범죄 1
 
2.9%
보건범죄 1
 
2.9%
환경범죄 1
 
2.9%
교통범죄 1
 
2.9%
노동범죄 1
 
2.9%
Other values (4) 4
11.4%

Length

2023-12-12T18:31:36.020733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지능범죄 9
25.7%
강력범죄 7
20.0%
폭력범죄 7
20.0%
풍속범죄 2
 
5.7%
절도범죄 1
 
2.9%
마약범죄 1
 
2.9%
보건범죄 1
 
2.9%
환경범죄 1
 
2.9%
교통범죄 1
 
2.9%
노동범죄 1
 
2.9%
Other values (4) 4
11.4%

중분류
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-12T18:31:36.218430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length3.7142857
Min length2

Characters and Unicode

Total characters130
Distinct characters71
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)100.0%

Sample

1st row살인미수등
2nd row강도
3rd row강간
4th row유사강간
5th row강제추행
ValueCountFrequency (%)
강간 2
 
5.0%
살인미수등 1
 
2.5%
통화 1
 
2.5%
보건범죄 1
 
2.5%
유가증권인지 1
 
2.5%
사기 1
 
2.5%
횡령 1
 
2.5%
배임 1
 
2.5%
성풍속범죄 1
 
2.5%
도박범죄 1
 
2.5%
Other values (29) 29
72.5%
2023-12-12T18:31:36.613060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11
 
8.5%
11
 
8.5%
6
 
4.6%
5
 
3.8%
4
 
3.1%
4
 
3.1%
4
 
3.1%
3
 
2.3%
3
 
2.3%
3
 
2.3%
Other values (61) 76
58.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 125
96.2%
Space Separator 5
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11
 
8.8%
11
 
8.8%
6
 
4.8%
4
 
3.2%
4
 
3.2%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
Other values (60) 73
58.4%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 125
96.2%
Common 5
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11
 
8.8%
11
 
8.8%
6
 
4.8%
4
 
3.2%
4
 
3.2%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
Other values (60) 73
58.4%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 125
96.2%
ASCII 5
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
11
 
8.8%
11
 
8.8%
6
 
4.8%
4
 
3.2%
4
 
3.2%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
Other values (60) 73
58.4%
ASCII
ValueCountFrequency (%)
5
100.0%

현행범
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2550.8571
Minimum0
Maximum29674
Zeros4
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T18:31:36.803774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116
median290
Q32786
95-th percentile9845.7
Maximum29674
Range29674
Interquartile range (IQR)2770

Descriptive statistics

Standard deviation5583.2138
Coefficient of variation (CV)2.1887599
Kurtosis16.612903
Mean2550.8571
Median Absolute Deviation (MAD)284
Skewness3.7381426
Sum89280
Variance31172276
MonotonicityNot monotonic
2023-12-12T18:31:36.971929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 4
 
11.4%
22 2
 
5.7%
248 1
 
2.9%
1798 1
 
2.9%
1 1
 
2.9%
9095 1
 
2.9%
355 1
 
2.9%
2295 1
 
2.9%
312 1
 
2.9%
3 1
 
2.9%
Other values (21) 21
60.0%
ValueCountFrequency (%)
0 4
11.4%
1 1
 
2.9%
3 1
 
2.9%
6 1
 
2.9%
9 1
 
2.9%
10 1
 
2.9%
22 2
5.7%
24 1
 
2.9%
46 1
 
2.9%
59 1
 
2.9%
ValueCountFrequency (%)
29674 1
2.9%
10727 1
2.9%
9468 1
2.9%
9095 1
2.9%
6631 1
2.9%
4572 1
2.9%
4182 1
2.9%
4104 1
2.9%
3277 1
2.9%
2295 1
2.9%

신고-피해자신고
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17158.371
Minimum0
Maximum281153
Zeros1
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T18:31:37.137447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.7
Q131
median388
Q36469.5
95-th percentile78051.4
Maximum281153
Range281153
Interquartile range (IQR)6438.5

Descriptive statistics

Standard deviation53673.081
Coefficient of variation (CV)3.1280988
Kurtosis18.964108
Mean17158.371
Median Absolute Deviation (MAD)384
Skewness4.2635664
Sum600543
Variance2.8807996 × 109
MonotonicityNot monotonic
2023-12-12T18:31:37.277034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
15 2
 
5.7%
251 2
 
5.7%
106 1
 
2.9%
44 1
 
2.9%
21087 1
 
2.9%
26340 1
 
2.9%
3268 1
 
2.9%
84 1
 
2.9%
388 1
 
2.9%
281153 1
 
2.9%
Other values (23) 23
65.7%
ValueCountFrequency (%)
0 1
2.9%
1 1
2.9%
2 1
2.9%
3 1
2.9%
4 1
2.9%
15 2
5.7%
16 1
2.9%
18 1
2.9%
44 1
2.9%
69 1
2.9%
ValueCountFrequency (%)
281153 1
2.9%
158725 1
2.9%
43477 1
2.9%
26340 1
2.9%
24056 1
2.9%
21087 1
2.9%
16077 1
2.9%
6658 1
2.9%
6583 1
2.9%
6356 1
2.9%

신고-고소
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6019.0286
Minimum2
Maximum102204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T18:31:37.445506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10.1
Q141.5
median342
Q32533.5
95-th percentile27204
Maximum102204
Range102202
Interquartile range (IQR)2492

Descriptive statistics

Standard deviation19094.941
Coefficient of variation (CV)3.1724291
Kurtosis20.537582
Mean6019.0286
Median Absolute Deviation (MAD)330
Skewness4.4161112
Sum210666
Variance3.6461679 × 108
MonotonicityNot monotonic
2023-12-12T18:31:37.641488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
30 1
 
2.9%
46 1
 
2.9%
166 1
 
2.9%
102204 1
 
2.9%
16422 1
 
2.9%
3384 1
 
2.9%
788 1
 
2.9%
12 1
 
2.9%
8 1
 
2.9%
363 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
2 1
2.9%
8 1
2.9%
11 1
2.9%
12 1
2.9%
15 1
2.9%
16 1
2.9%
19 1
2.9%
30 1
2.9%
37 1
2.9%
46 1
2.9%
ValueCountFrequency (%)
102204 1
2.9%
52362 1
2.9%
16422 1
2.9%
9910 1
2.9%
7431 1
2.9%
3478 1
2.9%
3454 1
2.9%
3384 1
2.9%
2948 1
2.9%
2119 1
2.9%

신고-고발
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1582.4571
Minimum0
Maximum24657
Zeros3
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T18:31:37.851258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median52
Q3421
95-th percentile8687
Maximum24657
Range24657
Interquartile range (IQR)410

Descriptive statistics

Standard deviation4950.9027
Coefficient of variation (CV)3.1286172
Kurtosis16.218737
Mean1582.4571
Median Absolute Deviation (MAD)51
Skewness4.0076586
Sum55386
Variance24511437
MonotonicityNot monotonic
2023-12-12T18:31:38.012652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 3
 
8.6%
11 2
 
5.7%
2 2
 
5.7%
25 2
 
5.7%
316 1
 
2.9%
52 1
 
2.9%
115 1
 
2.9%
5321 1
 
2.9%
2140 1
 
2.9%
1877 1
 
2.9%
Other values (20) 20
57.1%
ValueCountFrequency (%)
0 3
8.6%
1 1
 
2.9%
2 2
5.7%
5 1
 
2.9%
6 1
 
2.9%
11 2
5.7%
15 1
 
2.9%
17 1
 
2.9%
25 2
5.7%
33 1
 
2.9%
ValueCountFrequency (%)
24657 1
2.9%
16541 1
2.9%
5321 1
2.9%
2140 1
2.9%
1877 1
2.9%
1001 1
2.9%
953 1
2.9%
747 1
2.9%
461 1
2.9%
381 1
2.9%

신고-자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.37143
Minimum0
Maximum9575
Zeros8
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T18:31:38.144052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q321
95-th percentile215.5
Maximum9575
Range9575
Interquartile range (IQR)20

Descriptive statistics

Standard deviation1615.0396
Coefficient of variation (CV)5.3947688
Kurtosis34.899912
Mean299.37143
Median Absolute Deviation (MAD)4
Skewness5.9038971
Sum10478
Variance2608353
MonotonicityNot monotonic
2023-12-12T18:31:38.303800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 8
22.9%
1 6
17.1%
4 3
 
8.6%
2 3
 
8.6%
5 2
 
5.7%
48 1
 
2.9%
193 1
 
2.9%
9575 1
 
2.9%
268 1
 
2.9%
72 1
 
2.9%
Other values (8) 8
22.9%
ValueCountFrequency (%)
0 8
22.9%
1 6
17.1%
2 3
 
8.6%
4 3
 
8.6%
5 2
 
5.7%
7 1
 
2.9%
8 1
 
2.9%
9 1
 
2.9%
10 1
 
2.9%
32 1
 
2.9%
ValueCountFrequency (%)
9575 1
2.9%
268 1
2.9%
193 1
2.9%
136 1
2.9%
72 1
2.9%
48 1
2.9%
44 1
2.9%
42 1
2.9%
32 1
2.9%
10 1
2.9%

신고-진정,투서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3478.9429
Minimum0
Maximum94020
Zeros1
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T18:31:38.430584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.7
Q111
median82
Q3592
95-th percentile6407.1
Maximum94020
Range94020
Interquartile range (IQR)581

Descriptive statistics

Standard deviation15947.073
Coefficient of variation (CV)4.5838847
Kurtosis33.191646
Mean3478.9429
Median Absolute Deviation (MAD)80
Skewness5.7111087
Sum121763
Variance2.5430913 × 108
MonotonicityNot monotonic
2023-12-12T18:31:38.558835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
5 2
 
5.7%
19 2
 
5.7%
11 2
 
5.7%
0 1
 
2.9%
600 1
 
2.9%
2943 1
 
2.9%
81 1
 
2.9%
559 1
 
2.9%
226 1
 
2.9%
82 1
 
2.9%
Other values (22) 22
62.9%
ValueCountFrequency (%)
0 1
2.9%
2 1
2.9%
3 1
2.9%
4 1
2.9%
5 2
5.7%
7 1
2.9%
8 1
2.9%
11 2
5.7%
18 1
2.9%
19 2
5.7%
ValueCountFrequency (%)
94020 1
2.9%
14490 1
2.9%
2943 1
2.9%
2296 1
2.9%
1748 1
2.9%
830 1
2.9%
761 1
2.9%
604 1
2.9%
600 1
2.9%
584 1
2.9%

신고-타인신고
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2916.4571
Minimum1
Maximum60959
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T18:31:38.683432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.7
Q141.5
median298
Q3902
95-th percentile8996.3
Maximum60959
Range60958
Interquartile range (IQR)860.5

Descriptive statistics

Standard deviation10806.217
Coefficient of variation (CV)3.7052547
Kurtosis26.334097
Mean2916.4571
Median Absolute Deviation (MAD)293
Skewness5.0156301
Sum102076
Variance1.1677432 × 108
MonotonicityNot monotonic
2023-12-12T18:31:38.829105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2 2
 
5.7%
1 2
 
5.7%
5 2
 
5.7%
2244 1
 
2.9%
7 1
 
2.9%
558 1
 
2.9%
414 1
 
2.9%
693 1
 
2.9%
595 1
 
2.9%
298 1
 
2.9%
Other values (22) 22
62.9%
ValueCountFrequency (%)
1 2
5.7%
2 2
5.7%
5 2
5.7%
7 1
2.9%
23 1
2.9%
40 1
2.9%
43 1
2.9%
55 1
2.9%
59 1
2.9%
81 1
2.9%
ValueCountFrequency (%)
60959 1
2.9%
22836 1
2.9%
3065 1
2.9%
2415 1
2.9%
2244 1
2.9%
2169 1
2.9%
1038 1
2.9%
974 1
2.9%
914 1
2.9%
890 1
2.9%

미신고-불심검문
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6785.1714
Minimum0
Maximum200279
Zeros9
Zeros (%)25.7%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T18:31:38.997862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median3
Q365
95-th percentile11034.4
Maximum200279
Range200279
Interquartile range (IQR)64.5

Descriptive statistics

Standard deviation34197.697
Coefficient of variation (CV)5.0400638
Kurtosis32.702519
Mean6785.1714
Median Absolute Deviation (MAD)3
Skewness5.6611254
Sum237481
Variance1.1694825 × 109
MonotonicityNot monotonic
2023-12-12T18:31:39.124593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 9
25.7%
3 5
14.3%
1 4
11.4%
2 2
 
5.7%
37 1
 
2.9%
35519 1
 
2.9%
14 1
 
2.9%
200279 1
 
2.9%
98 1
 
2.9%
219 1
 
2.9%
Other values (9) 9
25.7%
ValueCountFrequency (%)
0 9
25.7%
1 4
11.4%
2 2
 
5.7%
3 5
14.3%
5 1
 
2.9%
9 1
 
2.9%
14 1
 
2.9%
35 1
 
2.9%
37 1
 
2.9%
61 1
 
2.9%
ValueCountFrequency (%)
200279 1
2.9%
35519 1
2.9%
541 1
2.9%
241 1
2.9%
219 1
2.9%
198 1
2.9%
133 1
2.9%
98 1
2.9%
69 1
2.9%
61 1
2.9%

미신고-피해품발견
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.228571
Minimum0
Maximum956
Zeros20
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T18:31:39.288484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.5
95-th percentile224.3
Maximum956
Range956
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation168.83059
Coefficient of variation (CV)3.9055326
Kurtosis26.867037
Mean43.228571
Median Absolute Deviation (MAD)0
Skewness5.0266445
Sum1513
Variance28503.77
MonotonicityNot monotonic
2023-12-12T18:31:39.402449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 20
57.1%
1 5
 
14.3%
4 3
 
8.6%
956 1
 
2.9%
3 1
 
2.9%
6 1
 
2.9%
281 1
 
2.9%
13 1
 
2.9%
37 1
 
2.9%
200 1
 
2.9%
ValueCountFrequency (%)
0 20
57.1%
1 5
 
14.3%
3 1
 
2.9%
4 3
 
8.6%
6 1
 
2.9%
13 1
 
2.9%
37 1
 
2.9%
200 1
 
2.9%
281 1
 
2.9%
956 1
 
2.9%
ValueCountFrequency (%)
956 1
 
2.9%
281 1
 
2.9%
200 1
 
2.9%
37 1
 
2.9%
13 1
 
2.9%
6 1
 
2.9%
4 3
 
8.6%
3 1
 
2.9%
1 5
 
14.3%
0 20
57.1%

미신고-변사체
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.457143
Minimum0
Maximum347
Zeros22
Zeros (%)62.9%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T18:31:39.502237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile17.3
Maximum347
Range347
Interquartile range (IQR)1

Descriptive statistics

Standard deviation58.530205
Coefficient of variation (CV)5.1086214
Kurtosis34.622584
Mean11.457143
Median Absolute Deviation (MAD)0
Skewness5.8708255
Sum401
Variance3425.7849
MonotonicityNot monotonic
2023-12-12T18:31:39.624372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 22
62.9%
1 5
 
14.3%
2 3
 
8.6%
4 2
 
5.7%
17 1
 
2.9%
18 1
 
2.9%
347 1
 
2.9%
ValueCountFrequency (%)
0 22
62.9%
1 5
 
14.3%
2 3
 
8.6%
4 2
 
5.7%
17 1
 
2.9%
18 1
 
2.9%
347 1
 
2.9%
ValueCountFrequency (%)
347 1
 
2.9%
18 1
 
2.9%
17 1
 
2.9%
4 2
 
5.7%
2 3
 
8.6%
1 5
 
14.3%
0 22
62.9%

미신고-탐문정보
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1200.6571
Minimum0
Maximum13597
Zeros1
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T18:31:39.805709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q134.5
median198
Q31133.5
95-th percentile4432.7
Maximum13597
Range13597
Interquartile range (IQR)1099

Descriptive statistics

Standard deviation2591.22
Coefficient of variation (CV)2.1581681
Kurtosis16.225088
Mean1200.6571
Median Absolute Deviation (MAD)192
Skewness3.8013141
Sum42023
Variance6714420.9
MonotonicityNot monotonic
2023-12-12T18:31:39.945508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
5 2
 
5.7%
13 1
 
2.9%
2866 1
 
2.9%
6 1
 
2.9%
2715 1
 
2.9%
1425 1
 
2.9%
152 1
 
2.9%
2240 1
 
2.9%
3203 1
 
2.9%
1620 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
0 1
2.9%
5 2
5.7%
6 1
2.9%
13 1
2.9%
14 1
2.9%
20 1
2.9%
21 1
2.9%
34 1
2.9%
35 1
2.9%
67 1
2.9%
ValueCountFrequency (%)
13597 1
2.9%
7302 1
2.9%
3203 1
2.9%
2866 1
2.9%
2715 1
2.9%
2240 1
2.9%
1620 1
2.9%
1499 1
2.9%
1425 1
2.9%
842 1
2.9%

미신고-여죄
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1308.6857
Minimum0
Maximum20593
Zeros4
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T18:31:40.098387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median72
Q3386.5
95-th percentile8398.8
Maximum20593
Range20593
Interquartile range (IQR)382.5

Descriptive statistics

Standard deviation4052.0138
Coefficient of variation (CV)3.0962467
Kurtosis16.511212
Mean1308.6857
Median Absolute Deviation (MAD)72
Skewness3.9815203
Sum45804
Variance16418816
MonotonicityNot monotonic
2023-12-12T18:31:40.246869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 4
 
11.4%
4 2
 
5.7%
11 2
 
5.7%
3 2
 
5.7%
7 1
 
2.9%
165 1
 
2.9%
20593 1
 
2.9%
2 1
 
2.9%
63 1
 
2.9%
390 1
 
2.9%
Other values (19) 19
54.3%
ValueCountFrequency (%)
0 4
11.4%
1 1
 
2.9%
2 1
 
2.9%
3 2
5.7%
4 2
5.7%
7 1
 
2.9%
10 1
 
2.9%
11 2
5.7%
46 1
 
2.9%
63 1
 
2.9%
ValueCountFrequency (%)
20593 1
2.9%
11931 1
2.9%
6885 1
2.9%
1275 1
2.9%
987 1
2.9%
793 1
2.9%
628 1
2.9%
405 1
2.9%
390 1
2.9%
383 1
2.9%

미신고-기타
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2881.4857
Minimum6
Maximum40066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T18:31:40.394425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9
Q125
median191
Q3788
95-th percentile14351
Maximum40066
Range40060
Interquartile range (IQR)763

Descriptive statistics

Standard deviation8915.9174
Coefficient of variation (CV)3.0942084
Kurtosis14.258822
Mean2881.4857
Median Absolute Deviation (MAD)176
Skewness3.8806389
Sum100852
Variance79493583
MonotonicityNot monotonic
2023-12-12T18:31:40.547118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
25 2
 
5.7%
20 2
 
5.7%
9 2
 
5.7%
193 1
 
2.9%
61 1
 
2.9%
1587 1
 
2.9%
4949 1
 
2.9%
2589 1
 
2.9%
2779 1
 
2.9%
817 1
 
2.9%
Other values (22) 22
62.9%
ValueCountFrequency (%)
6 1
2.9%
9 2
5.7%
11 1
2.9%
20 2
5.7%
23 1
2.9%
24 1
2.9%
25 2
5.7%
31 1
2.9%
37 1
2.9%
44 1
2.9%
ValueCountFrequency (%)
40066 1
2.9%
36289 1
2.9%
4949 1
2.9%
3981 1
2.9%
3352 1
2.9%
2779 1
2.9%
2589 1
2.9%
1587 1
2.9%
817 1
2.9%
759 1
2.9%

Interactions

2023-12-12T18:31:33.778061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:15.725584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:17.087938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:18.494899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:20.283497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:21.859900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:23.314399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:24.717078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:26.202597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:28.125934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:29.649608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:31.002434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:32.491423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:33.887966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:15.849686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:17.191704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:18.610466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:20.771382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:21.961918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:23.436606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:24.831726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:26.322365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:28.238220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:29.752131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:31.111892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:32.615946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:33.998841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:15.970635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:17.276202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:18.704177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:20.859644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:22.086185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:23.519688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:24.944919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:26.433436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:28.369264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:29.837006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:31.211915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:32.722878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:34.112513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:16.080608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:17.460729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:18.833142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:20.950552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:22.256736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:23.613923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:25.035028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:26.548203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:28.496590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:29.930423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:31.301843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:32.813708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:34.213779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:16.166912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:17.564502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:18.943373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:21.037256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:22.376422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:23.707904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:25.153212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:26.683825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:28.626275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:30.039443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:31.407347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:32.898189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:34.330338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:16.260212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:17.658128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:19.048286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:21.126197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:22.496644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:23.823767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:25.297169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:26.798072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:28.729904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:30.126648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:31.524378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:32.990891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:34.444201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:16.353271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:17.745194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:19.149052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:21.207889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:22.590279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:23.937444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:25.387757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:26.903486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:28.829764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:30.221895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:31.621813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:33.072425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:34.570741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:16.451000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:17.837774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:19.296760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:21.301710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:22.701878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:24.051920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:25.502838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:27.010430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:28.952613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:30.337390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:31.745492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:33.158828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:34.697974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:16.565404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:17.933083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:19.499991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:21.396512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:22.815906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:24.176845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:25.614201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:27.153530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:29.084522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:30.468408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:31.887989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:33.267126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:34.803364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:16.664107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:18.019964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:19.664353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:21.491903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:22.901746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:24.273899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:25.728407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:27.269812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:29.175458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:30.572459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:31.993334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:33.357988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:35.230383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:16.773352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:18.119288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:19.830575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:21.595928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:22.997311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:24.374073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:25.865123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:27.393721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:29.299031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:30.673333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:32.103208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:33.464163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:35.336475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:16.894266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:18.241062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:19.963162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:21.683752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:23.094909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:24.509603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:25.982807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:27.564993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:29.431879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:30.782372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:32.224448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:33.564587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:35.429969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:16.991669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:18.352163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:20.112481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:21.759721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:23.198358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:24.594203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:26.080158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:27.677405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:29.532666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:30.893090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:32.342505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:31:33.671739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:31:40.673864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류중분류현행범신고-피해자신고신고-고소신고-고발신고-자수신고-진정,투서신고-타인신고미신고-불심검문미신고-피해품발견미신고-변사체미신고-탐문정보미신고-여죄미신고-기타
대분류1.0001.0000.8430.8840.3061.0001.0000.7001.0001.0000.8901.0000.9240.8770.877
중분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
현행범0.8431.0001.0000.7300.6740.5780.2930.7730.7500.7500.8171.0000.9450.8720.525
신고-피해자신고0.8841.0000.7301.0000.0000.0001.0000.0000.6490.6490.6500.0000.5780.8620.424
신고-고소0.3061.0000.6740.0001.0000.8620.0001.0000.6490.6490.6491.0000.6850.9780.424
신고-고발1.0001.0000.5780.0000.8621.0000.0000.6490.6490.6490.4241.0000.6330.8620.424
신고-자수1.0001.0000.2931.0000.0000.0001.0000.0001.0001.0000.0000.0000.0000.0000.432
신고-진정,투서0.7001.0000.7730.0001.0000.6490.0001.0000.9340.9340.7831.0000.7361.0000.783
신고-타인신고1.0001.0000.7500.6490.6490.6491.0000.9341.0001.0000.7831.0000.6860.6490.934
미신고-불심검문1.0001.0000.7500.6490.6490.6491.0000.9341.0001.0000.7831.0000.6860.6490.934
미신고-피해품발견0.8901.0000.8170.6500.6490.4240.0000.7830.7830.7831.0000.4320.8380.7880.556
미신고-변사체1.0001.0001.0000.0001.0001.0000.0001.0001.0001.0000.4321.0001.0001.0000.432
미신고-탐문정보0.9241.0000.9450.5780.6850.6330.0000.7360.6860.6860.8381.0001.0000.8510.675
미신고-여죄0.8771.0000.8720.8620.9780.8620.0001.0000.6490.6490.7881.0000.8511.0000.424
미신고-기타0.8771.0000.5250.4240.4240.4240.4320.7830.9340.9340.5560.4320.6750.4241.000
2023-12-12T18:31:40.878563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
현행범신고-피해자신고신고-고소신고-고발신고-자수신고-진정,투서신고-타인신고미신고-불심검문미신고-피해품발견미신고-변사체미신고-탐문정보미신고-여죄미신고-기타대분류
현행범1.0000.8940.5580.0480.8340.5600.8900.6710.6400.4640.7070.6900.7210.531
신고-피해자신고0.8941.0000.6230.0610.7540.6380.8110.5770.7360.3680.5840.6210.6350.595
신고-고소0.5580.6231.0000.3220.4470.6940.4470.4320.557-0.0070.5200.5060.4080.099
신고-고발0.0480.0610.3221.0000.2450.6770.2680.6090.406-0.1510.3560.3760.5460.823
신고-자수0.8340.7540.4470.2451.0000.5850.8680.7300.7350.5400.7430.7470.8340.798
신고-진정,투서0.5600.6380.6940.6770.5851.0000.6620.7190.7520.0100.6510.6230.7890.412
신고-타인신고0.8900.8110.4470.2680.8680.6621.0000.7790.6900.4930.7190.7310.8540.810
미신고-불심검문0.6710.5770.4320.6090.7300.7190.7791.0000.6390.2500.7730.7870.8800.810
미신고-피해품발견0.6400.7360.5570.4060.7350.7520.6900.6391.0000.2300.5550.5510.6990.637
미신고-변사체0.4640.368-0.007-0.1510.5400.0100.4930.2500.2301.0000.3440.2730.3380.798
미신고-탐문정보0.7070.5840.5200.3560.7430.6510.7190.7730.5550.3441.0000.8400.8310.662
미신고-여죄0.6900.6210.5060.3760.7470.6230.7310.7870.5510.2730.8401.0000.8200.585
미신고-기타0.7210.6350.4080.5460.8340.7890.8540.8800.6990.3380.8310.8201.0000.618
대분류0.5310.5950.0990.8230.7980.4120.8100.8100.6370.7980.6620.5850.6181.000

Missing values

2023-12-12T18:31:35.610399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:31:35.852092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

대분류중분류현행범신고-피해자신고신고-고소신고-고발신고-자수신고-진정,투서신고-타인신고미신고-불심검문미신고-피해품발견미신고-변사체미신고-탐문정보미신고-여죄미신고-기타
0강력범죄살인미수등2481063004012610113425
1강력범죄강도18561146128811041394625
2강력범죄강간47619371247657637610484264121
3강력범죄유사강간592511100054000094420
4강력범죄강제추행418266582119431019689035101499165256
5강력범죄기타 강간 강제추행등4617849223430026779
6강력범죄방화50255215052354201141144
7절도범죄절도663115872529485513617482415541956213597119313352
8폭력범죄상해1072724056991033447613065541762072714
9폭력범죄체포 감금30250829315018113100341024
대분류중분류현행범신고-피해자신고신고-고소신고-고발신고-자수신고-진정,투서신고-타인신고미신고-불심검문미신고-피해품발견미신고-변사체미신고-탐문정보미신고-여죄미신고-기타
25풍속범죄도박범죄17982511225722262244370132036284949
26마약범죄마약범죄312848115268115956113228664052589
27보건범죄보건범죄4063883635321460021692190016207932779
28환경범죄환경범죄104455214018229698001551275193
29교통범죄교통범죄45722811535751877957558460959200279371831639040066
30노동범죄노동범죄221647381127394140172863817
31안보범죄안보범죄641151450005337
32선거범죄선거범죄24188646114059300255269
33병역범죄병역범죄03191654106023000023
34기타범죄기타범죄2967416077523622465719314490228363551920034773022059336289